AbstractA new approach to inference in belief networks has been recently proposed, which is based on an algebraic representation of belief networks using multi-linear functions. According to this approach, belief network inference reduces to a simple process of evaluating and differentiating multi-linear functions. We show here that mainstream inference algorithms based on jointrees are a special case of the approach based on multi-linear functions, in a very precise sense. We use this result to prove new properties of jointree algorithms. We also discuss some practical and theoretical implications of this new finding
Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in t...
Graduation date: 1999Bayesian networks are used for building intelligent agents that act under uncer...
A new approach for learning Bayesian belief networks from raw data is presented. The approach is bas...
AbstractA new approach to inference in belief networks has been recently proposed, which is based on...
AbstractIn the existing evidential networks applicable to belief functions, the relations among the ...
AbstractInference algorithms in directed evidential networks (DEVN) obtain their efficiency by makin...
This paper describes a general scheme for accomodating different types of conditional distributions ...
Belief networks are directed acyclic graphs in wh ch the nodes represent propositions (or variables)...
AbstractA number of exact algorithms have been developed in recent years to perform probabilistic in...
This article was reprinted in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, 1990, ...
This paper addresses the duality between the deterministic feed-forward neural networks (FF-NNs) and...
AbstractCutset conditioning and clique-tree propagation are two popular methods for exact probabilis...
Due to significant limitations of rule-based extensional decision-support systems researchers are lo...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
In the existing evidential networks applicable to belief functions, the relations among the variable...
Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in t...
Graduation date: 1999Bayesian networks are used for building intelligent agents that act under uncer...
A new approach for learning Bayesian belief networks from raw data is presented. The approach is bas...
AbstractA new approach to inference in belief networks has been recently proposed, which is based on...
AbstractIn the existing evidential networks applicable to belief functions, the relations among the ...
AbstractInference algorithms in directed evidential networks (DEVN) obtain their efficiency by makin...
This paper describes a general scheme for accomodating different types of conditional distributions ...
Belief networks are directed acyclic graphs in wh ch the nodes represent propositions (or variables)...
AbstractA number of exact algorithms have been developed in recent years to perform probabilistic in...
This article was reprinted in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, 1990, ...
This paper addresses the duality between the deterministic feed-forward neural networks (FF-NNs) and...
AbstractCutset conditioning and clique-tree propagation are two popular methods for exact probabilis...
Due to significant limitations of rule-based extensional decision-support systems researchers are lo...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
In the existing evidential networks applicable to belief functions, the relations among the variable...
Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in t...
Graduation date: 1999Bayesian networks are used for building intelligent agents that act under uncer...
A new approach for learning Bayesian belief networks from raw data is presented. The approach is bas...